Gaming content recommendation for a video game

ABSTRACT

Systems and methods for providing game content recommendation of a multiplayer online battle arena (MOBA) video game based on player&#39;s performance are disclosed. Prior to an actual MOBA video game play, a player data stored for each player of each team is analyzed to evaluate a video game session content. The player data is associated with the video game play session and stored settings for the MOBA video game. The player data includes attack metrics, defense metrics, and damage metrics to determine to evaluate the video game play session content. During the actual MOBA video game play, an in-game performance of each player of each team in the video game play session content is analyzed to recommend the video game plays session. Such analysis is based on game map of the video game play session and in game metrics determined from the video game play session.

BACKGROUND

The present disclosure relates to systems for content recommendationand, more particularly, to systems and related processes forrecommending gaming content, such as video game play session content fora video game based on players' performance.

SUMMARY

Video game play content may be recommended to a user based on the user'swatching history of the video games, genre of the games, and rating ofthe teams played in the video games. With some recommendation engines,however, such recommendations of the game play maybe biased. Forexample, a particular team may be top rated compared to another team,but that team may not have played their best in that game. Therecommendation engine will not evaluate the game based on how that gamewas played by a player or the entire team for a particular genre at aparticular level.

In view of the foregoing, the present disclosure provides systems andrelated methods that recommend video game play content session contentfor a video game based on a player's and/or team's performance in thegame. In some embodiments, the recommendation is based on sequentialorder of a pre-game analysis, an in-game analysis, and a post-gameanalysis of the video play game play content.

In one example, a system compares information in the post-game analysiswith information in the pre-game analysis to determine whether torecommend the video game play content. For example, the system analyzesa particular player's statistics before the game (pre-game analysis) anddetermines how that player played in an actual game (in-game analysis).Assuming the player played well in the actual game, the system analyzesthe player's statistics (e.g. no. of kills, no. of saves etc.) in thepost-game analysis and determines whether the statistics areapproximately same as the player's statistics in the pre-game analysis.In other words, if system determines whether the player's performanceduring the in-game analysis is close to performance in the pre-gameanalysis. If it is determined that the players' performance isdetermined to be close, then the system recommends video game playcontent of the actual video game.

In another example, the system determines, in the pre-game analysis,whether a game play of a video game is interesting or not. For example,in the pre-game analysis, the system would analyze to determine that aplayer performance was really good, e.g. player killed 20 dragons in 30minutes and thus would find the video game to be interesting. If thegame play is determined to be interesting, then the system would furtherevaluate the game play of the video game in the in-game analysis. In thein-game analysis, the system would for example, determine the sameplayer's performance to be good also, e.g. the player killed 19 dragonsin 30 minutes. Then, the system recommends the user to watch the videogame play content of the actual video game.

The method, in one aspect, includes calculating a pre-game performancemetric based on stored player data and on stored settings for the videogame. A determination is made that the pre-game performance metric meetsa pre-game threshold. In response to determining that the pre-gameperformance metric meets the pre-game threshold, an in-game performancemetric is calculated based on stored metadata associated with the videogame play session content that is indicative of aspects of game play.Also, a determination is made that the in-game performance metric meetsan in-game performance threshold. In response to determining that thein-game performance metric meets the in-game performance threshold,aggregated statistics from the video game play session content aredetermined. The method further analyzes the aggregated statisticrelative to the stored player data and stored settings for the videogame and based on the analyzing, determines whether to recommend thevideo game play session content.

In another aspect, the method includes evaluating video game playsession content for a multiplayer online battle arena (MOBA) video game.Player data for each player of each team associated with the video gameplay session and stored settings for the MOBA video game is analyzed todetermine to evaluate the video game play session content. The playerdata includes metrics related to attack metrics, defense metrics, anddamage metrics. In response to determining to evaluate the video gameplay session content, an in-game performance of each player of each teamin the video game play session content is analyzed based on a game mapof the video game play session content and on in-game metrics for eachplayer of each team determined from the video game play session content.The in-game metrics includes in-game attack metrics, in-game defensemetrics, and in-game damage metrics. The method further determineswhether to recommend the video game play session content based on thein-game performance of each player of each team.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects and advantages of the disclosure will beapparent upon consideration of the following detailed description, takenin conjunction with the accompanying drawings, in which:

FIG. 1 is an illustrative block diagram of a system for recommendinggaming content of a video game, in accordance with some embodiments ofthe disclosure;

FIG. 2 is an illustrative block diagram of a system for recommendinggaming content of a multiplayer online battle arena (MOBA) video game,in accordance with some embodiments of the disclosure;

FIG. 3 is an illustrative block diagram showing additional details ofthe systems of FIG. 1 and/or FIG. 2, in accordance with some embodimentsof the disclosure;

FIG. 4 is an illustrative flow chart of a process for recommending videogame play session content for a video game, in accordance with someembodiments of the disclosure;

FIG. 5 is an illustrative flowchart of pre-game analysis of analyzingdata of the video game content prior to video game play, in accordancewith some embodiments of the disclosure;

FIG. 6 is an illustrative flowchart of in-game analysis of analyzingdata of the video game content during video game play session, inaccordance with some embodiments of the disclosure;

FIG. 7 is an illustrative flowchart of post-game analysis of analyzingresults of the in-game analysis to recommend video game play sessioncontent for the video game, in accordance with some embodiments of thedisclosure;

FIG. 8 illustrates an example of a table structure of pre-gameperformance metrics of pre-game analysis of the video game, inaccordance with some embodiments of the disclosure;

FIG. 9 illustrates an example of graph structure of pre-game metrics ofpre-game analysis of the video game, in accordance with some embodimentsof the disclosure;

FIG. 10A illustrates an example of a table structure of in-gameperformance metrics of an in-game analysis of the video game, inaccordance with some embodiments of the disclosure;

FIG. 10B illustrates an example of a table structure of aggregatedin-game performance metrics of the in-game performance metrics of FIG.10A.

FIG. 10C illustrates an example of a graph structure of aggregatedin-game performance metrics of the in-game performance metrics of FIG.10A.

FIG. 11A shows an example of graph structure of post-game performancemetrics of players in the post-game analysis of the video game, inaccordance with some embodiments of the disclosure;

FIG. 11B shows an example of a graph structure of aggregated post-gameperformance metrics of players in post-game analysis of the video gamein accordance with some embodiments of the disclosure;

FIG. 11C shows an example of table structure of aggregated post-gameperformance metrics of teams in-post analysis of the video game,accordance with some embodiments of the disclosure;

FIG. 12 is an illustrative flowchart of a process for recommending avideo game in accordance with another embodiment of the disclosure;

FIG. 13 is an illustrative flowchart of a process for analyzing data ofthe video game content prior to video game play, in accordance with someembodiments of the disclosure; and

FIG. 14 is an illustrative flowchart of in-game analysis of analyzingdata of the video game content during video game play session, inaccordance with some embodiments of the disclosure.

FIG. 15 is an illustrative flowchart of post-game analysis of analyzingdata of the video game content, in accordance with some embodiments ofthe disclosure.

DETAILED DESCRIPTION

FIG. 1 shows an illustrative block diagram of system 100 forrecommending game play session content for a video game, in accordancewith some embodiments of the disclosure. System 100 includes server 102,video gaming devices 104, communication network 106, content source ordatabase 108, video game settings database 110, metadata database 112and computing device 114. Although FIG. 1 shows content source 108,video game settings database 110, and metadata database 112 asindividual components and as separate from server 102, in someembodiments, any of those components may be combined and/or integratedas one device with server 102. In one embodiment, server 102 iscommunicatively coupled to video gaming devices 104, content source 108,the video game settings database 110 and metadata database 112 by way ofadditional communication paths, which may be included in communicationnetwork 106 or may be separate from communication network 106.Communication network 106 may be any type of communication network, suchas the Internet, a mobile phone network, mobile voice or data network(e.g., a 4G or LTE network), cable network, public switched telephonenetwork, or any combination of two or more of such communicationnetworks. Communication network 106 includes one or more communicationpaths, such as a satellite path, a fiber-optic path, a cable path, apath that supports Internet communications (e.g., IPTV), free-spaceconnections (e.g., for broadcast or other wireless signals), or anyother suitable wired or wireless communication path or combination ofsuch paths, such as a proprietary communication path and/or network 103.Network 106, in various aspects, may include the Internet or any othersuitable network or group of networks.

In one embodiment, the server 102 is communicably coupled to thecomputing device 114 by way of the communication network 106. Someexample types of computing device 102 include, without limitation, agaming device (such as a PLAYSTATION device, an XBOX device, or anyother gaming device), a smartphone, a tablet, a personal computer, aset-top box (STB), a digital video recorder (DVR), and/or the like, thatprovides various user interfaces configured to receive and view contentand/or interact with the server 102 and/or the video gaming device(s)104. In some examples, computing device 102 provides a display, which isconfigured to display information via a graphical user interface.

In one embodiment, the server 102 is configured to aggregate overcommunication network 106, from a variety sources, such video gamingdevices 104, content that helps to evaluate the video game play sessioncontent of the video game for a particular genre at a particular level.Some different types of genres of video games include action, adventure,horror, sports, role play, strategy, puzzle, board, and any combinationsof these genres. Server 102 evaluates the video game play sessioncontent based on how the game was played. For example, server 102 mayreceive content, such as challenge tutorials or video clips of actualgame play played in the video gaming devices 104, that shows how thegame was played by player(s) and/or the entire team in particular videogames or segments thereof. In some embodiments, the server 102 functionsto recommend video game play session content to a user of a computingdevice 114. In one embodiment, the recommendation is based on sequentialorder of pre-game analysis, in-game analysis, and post-game analysis ofthe video game play content. The server 102 compares information in thepost-game analysis with information in the post-game analysis todetermine whether to recommend the video game play content.

In some embodiments, during the pre-game analysis, the server 102retrieves player data from the content 108. In one embodiment, theplayer data includes names/characters of one or more players and playermeta data. In one example, the player metadata includes aggregatedstatistics of combined measurement of performance skills of theplayer(s) prior to the game play (e.g. FIG. 9). In some embodiments, theserver retrieves stored video game settings 110 of the video game. Somedifferent types of settings include physical, temporal, emotional,ethical, and environmental. The settings may vary depending on the genreof the game, in one embodiment, the server 102 calculates in-gameperformance metrics based on the player data and the video gamesettings. In one example, the pre-game performance metrics measuresperformance of the team(s) using the existing player data and the videosettings. The pre-game performance metric measures performance of theteams with respect to the skills of their respective players prior tothe actual game play. The pre-game performance metric measuresperformance of the teams prior to the actual game play (e.g. FIG. 8).The pre-game performance metric is compared to a pre-game threshold(pre-determined) to determine likelihood of a video game session ofinterest. In one embodiment, upon determination of the likelihood of thevideo game session of interest, stored player data of the video game isused for post-game analysis as described below.

In some embodiments, during the in-game analysis, the server 102retrieves metadata 112 corresponding to video game play session content,which indicates aspects of game play. The metadata includes game playmetadata of the video game session. The game play metadata includesparameters that measure abilities of the teams during actual game play.In one embodiment, the server 102 uses the game play metadata tocalculate in-game performance metrics of the game play based on gameprogression during the video game session. The in-game performancemetrics measures various skills performed during the video game sessionof the actual game play. Some examples of the in-game performance skillsinclude character positioning (CP), route followed (RF), reaction time(RT) etc. The in-game performance metric is compared to an in-gamethreshold (pre-determined) to determine a compelling game play. In oneembodiment, the server determines an aggregated statistics of each ofthe skills of each of the players from video game play session content.In one example, the aggregated statistics is combined measurement ofperformance skills of the player(s) during the video game session of theactual game play. In one example, the aggregated statistics is combinedmeasurement of performance skills of the teams during the video gamesession of the actual game play (e.g. FIGS. 10A, 10B & 10C).

In some embodiments, during the post-game analysis, the server 102compares the aggregated statistics from the video game play sessioncontent of the in-game analysis with the aggregated statistics prior tothe game play stored in the player data of the video game selected inthe pre-game analysis. In one embodiment, the server determines that theaggregated statistics from the video play session content isapproximately equivalent to the aggregated statistics of the player dataand recommends the video play session content to a user of the computingdevice 114.

FIG. 2 shows an illustrative block diagram of system 200 forrecommending content based on mobile online battle arena multiplayer(MOBA) video game, in accordance with some embodiments of thedisclosure. In various embodiments, system 200 includes some componentsdescribed above in connection with system 100. In particular, system 200includes server 102, video gaming devices 104, communication network106, content source 108, video game settings database 110, metadatadatabase 112 and the computing device 114. Although FIG. 2 shows contentsource 108, video game settings database 110, and metadata database 112as individual components and as separate from server 102, in someembodiments, any of those components may be combined and/or integratedas one device with server 102. As shown, server 102 is communicativelycoupled to gaming devices 104 by way of communication network 106 and iscommunicatively coupled to content source 108, metadata database 110,gaming log database 202 by way of additional communication paths, whichmay be included in communication network 106 or may be separate fromcommunication network 106.

As illustrated in FIG. 2, in one embodiment, the content 108 includesMOBA content 208, which includes player data corresponding to a MOBAvideo game. In one example, the MOBA content includes player data suchas aggregated pre-game metrics for each of the players in the teams(e.g. FIG. 9). The aggregated pre-game metrics is a combined measurementof performance skills (e.g. defense skills, attack skills, damageskills, healing skills, control skills) of the player(s) prior to theMOBA video game play. Some examples of the pre-game metrics includepre-game damage metrics, pre-game attack metrics, pre-game defensemetrics, pre-game crowd control metrics, pregame global ability metricsetc. In one embodiment, the video settings 110 include MOBA video gamesettings 210. In one example the MOBA video game setting is a battleground with two separate teams of multiple players. In one embodiment,the metadata 212 includes MOBA metadata of a game map and parameters ofthe MOBA video game play. Some of the parameters include characterpositioning, route followed, reaction time, objective captured, timeelapsed in objective capturing and target destroyed. The characterpositioning includes positioning of the characters as soon as the gamestarts for both the teams. The route followed is the route followed bythe players in the teams. The reaction time is time it takes for theplayer/team to reach objectives and participate in fights. The objectivecaptured is the number of objectives captured by the players/teams. Timeelapsed is the time it took for the player/teams to capture theobjective.

In one embodiment, the server 102 executes a pre-game analysis of theMOBA video game play to evaluate a MOBA video game play session content.For example, the server 102 determines in the pre-game analysis whetherthe MOBA video game is interesting or not. The server utilizes the MOBAvideo game setting 212 and the aggregated pre-game metrics from MOBAcontent 208 for each of the players in the team to determine pre-gameperformance metric of each of the teams. In one example, the server 102utilizes a graph including the aggregated pre-game metrics (e.g. FIG. 9)of the pre-game analysis of three players in a team. The pre-gameperformance metric measures performance of the teams prior to the actualMOBA video game play (e.g. FIG. 8). The pre-game performance metric iscompared to a pre-game threshold (pre-determined) to determinelikelihood of a MOBA video game session of interest. In one embodiment,upon determination of the likelihood of the MOBA video game session ofinterest, the aggregated pre-game metrics of the video game is used forpost-game analysis as described below.

In one embodiment, the server 102 executes an in-game analysis of theMOBA video game. The server 102 utilizes the game map and the parametersfrom the MOBA metadata 212 to evaluate the MOBA video game sessioncontent in an actual MOBA video game session. Specifically, the server102 calculates in-game performance metrics of video game session contentutilizing the game map and the parameters. Some examples of the in-gameperformance metrics include route calculation, number of objectivescaptured etc. In one example, the server 102 utilizes the route followedparameter to calculate the route that was followed to reach the target.In another example, the sever 102 utilizes the objective capturedparameter to calculate number of objects captured by the players/teams.In one example, the in-game performance metrics is compared to anin-game threshold (pre-determined) to determine a compelling game play.In one embodiment, the server 102 determines in-game performance metricsof each of the teams from MOBA video game play session content. Thein-game performance metrics are measurement of team's performance duringthe actual MOBA video game play. The in-game performance metricscorrespond to the team's skills during the MOBA video game play session.In one embodiment, the server 102 calculates an in-game metrics of eachof the teams during the MOBA video game session of the actual MOBA gameplay (e.g. FIG. 10A). The in-game metrics represent the teams' skillsduring the actual MOBA video game play. Some examples of the in-gamemetrics include in-game damage metrics, in-game attack metrics, in-gamedefense metrics, in-game crowd control metrics, in-game global abilitymetrics etc. The server combines the in-game metrics of each of theteams to calculated aggregated statistics from the video game playsession content in-game analysis (e.g. FIGS. 10B & 10C).

In one embodiment, during the post-game analysis, the server 102compares the aggregated statistics from the in-game analysis with theaggregated statistics of the pre-game analysis. In one embodiment, theserver determines that the aggregated statistics of the in-game analysisfrom the video play session content is approximately equivalent to theaggregated statistics from pre-game analysis and recommends the MOBAvideo play session content to a user of the computing device 114.

FIG. 3 is an illustrative block diagram showing additional details ofsystem 100 (FIG. 1) and/or system 200 (FIG. 2), in accordance with someembodiments of the disclosure. In various embodiments, system 200includes some components described above in connection with system 100.Although FIG. 3 shows certain numbers of components, in variousexamples, system 300 may include fewer than the illustrated componentsand/or multiples of one or more illustrated components. Server 102includes control circuitry 302 and I/O path 308, and control circuitry302 includes storage 304 and processing circuitry 306. Computing device104, which may correspond to video gaming device 104 of FIG. 1 and FIG.2, may be a gaming device, such as a video game console, user televisionequipment such as a set-top box, user computer equipment, a wirelessuser communications device such as a smartphone device, or any device onwhich video games may be played. Computing device 104 includes controlcircuitry 310, I/O path 316, speaker 318, display 320, and user inputinterface 322. Control circuitry 310 includes storage 312 and processingcircuitry 314. Control circuitry 302 and/or 310 may be based on anysuitable processing circuitry such as processing circuitry 306 and/or314. As referred to herein, processing circuitry should be understood tomean circuitry based on one or more microprocessors, microcontrollers,digital signal processors, programmable logic devices,field-programmable gate arrays (FPGAs), application-specific integratedcircuits (ASICs), etc., and may include a multi-core processor (e.g.,dual-core, quad-core, hexa-core, or any suitable number of cores). Insome embodiments, processing circuitry may be distributed acrossmultiple separate processors, for example, multiple of the same type ofprocessors (e.g., two Intel Core i9 processors) or multiple differentprocessors (e.g., an Intel Core i7 processor and an Intel Core i9processor).

Each of storage 304, storage 312, and/or storages of other components ofsystem 300 (e.g., storages of content source 108, video game settings110, metadata database 112, and/or the like) may be an electronicstorage device. As referred to herein, the phrase “electronic storagedevice” or “storage device” should be understood to mean any device forstoring electronic data, computer software, or firmware, such asrandom-access memory, read-only memory, hard drives, optical drives,digital video disc (DVD) recorders, compact disc (CD) recorders, BLU-RAYdisc (BD) recorders, BLU-RAY 3D disc recorders, digital video recorders(DVRs, sometimes called personal video recorders, or PVRs), solid statedevices, quantum storage devices, gaming consoles, gaming media, or anyother suitable fixed or removable storage devices, and/or anycombination of the same. Each of storage 304, storage 312, and/orstorages of other components of system 300 may be used to store varioustypes of content, metadata, gaming data, media guidance data, and orother types of data. Non-volatile memory may also be used (e.g., tolaunch a boot-up routine and other instructions). Cloud-based storagemay be used to supplement storages 304, 312 or instead of storages 304,312. In some embodiments, control circuitry 302 and/or 310 executesinstructions for an application stored in memory (e.g., storage 304and/or 312). Specifically, control circuitry 302 and/or 310 may beinstructed by the application to perform the functions discussed herein.In some implementations, any action performed by control circuitry 302and/or 310 may be based on instructions received from the application.For example, the application may be implemented as software or a set ofexecutable instructions that may be stored in storage 304 and/or 312 andexecuted by control circuitry 302 and/or 310. In some embodiments, theapplication may be a client/server application where only a clientapplication resides on computing device 104, and a server applicationresides on server 102.

The application may be implemented using any suitable architecture. Forexample, it may be a stand-alone application wholly implemented oncomputing device 104. In such an approach, instructions for theapplication are stored locally (e.g., in storage 312), and data for useby the application is downloaded on a periodic basis (e.g., from anout-of-band feed, from an Internet resource, or using another suitableapproach). Processing circuitry 314 may retrieve instructions for theapplication from storage 312 and process the instructions to perform thefunctionality described herein. Based on the processed instructions,processing circuitry 314 may determine what action to perform when inputis received from user input interface 322.

In client/server-based embodiments, control circuitry 310 may includecommunication circuitry suitable for communicating with an applicationserver (e.g., server 102) or other networks or servers. The instructionsfor carrying out the functionality described herein may be stored on theapplication server. Communication circuitry may include a cable modem,an integrated services digital network (ISDN) modem, a digitalsubscriber line (DSL) modem, a telephone modem, an Ethernet card, or awireless modem for communication with other equipment, or any othersuitable communication circuitry. Such communication may involve theInternet or any other suitable communication networks or paths (e.g.,communication network 106). In another example of a client/server-basedapplication, control circuitry 310 runs a web browser that interpretsweb pages provided by a remote server (e.g., server 102). For example,the remote server may store the instructions for the application in astorage device. The remote server may process the stored instructionsusing circuitry (e.g., control circuitry 302) and generate the displaysdiscussed above and below. Computing device 104 may receive the displaysgenerated by the remote server and may display the content of thedisplays locally via display 320. This way, the processing of theinstructions is performed remotely (e.g., by server 102) while theresulting displays, such as the display windows described elsewhereherein, are provided locally on computing device 104. Computing device104 may receive inputs from the user via input interface 322 andtransmit those inputs to the remote server for processing and generatingthe corresponding displays.

A user may send instructions to control circuitry 302 and/or 310 usinguser input interface 322. User input interface 322 may be any suitableuser interface, such as a gaming controller, a remote control,trackball, keypad, keyboard, touchscreen, touchpad, stylus input,joystick, voice recognition interface, or other user input interfaces.User input interface 322 may be integrated with or combined with display320, which may be a monitor, a television, a liquid crystal display(LCD), an electronic ink display, or any other equipment suitable fordisplaying visual images.

Server 102 and computing device 104 may receive content and data viainput/output (hereinafter “I/O”) paths 308 and 316, respectively. Forinstance, I/O path 316 may include a communication port configured toreceive a live content stream from server 102 and/or content source 108via a communication network 106. Storage 312 may be configured to bufferthe received live content stream for playback and display 320 may beconfigured to present the buffered content, navigation options, alerts,and/or the like via a primary display window and/or a secondary displaywindow. I/O paths 308, 316 may provide content (e.g., a live stream ofcontent, broadcast programming, on-demand programming, Internet content,content available over a local area network (LAN) or wide area network(WAN), and/or other content) and data to control circuitry 302, 310.Control circuitry 302, 310 may be used to send and receive commands,requests, and other suitable data using I/O paths 308, 316. I/O paths308, 316 may connect control circuitry 302, 310 (and specificallyprocessing circuitry 306, 314) to one or more communication paths(described below). I/O functions may be provided by one or more of thesecommunication paths but are shown as single paths in FIG. 3 to avoidovercomplicating the drawing.

Content source 108 may include one or more types of content distributionequipment including a television distribution facility, cable systemheadend, satellite distribution facility, programming sources (e.g.,television broadcasters, such as NBC, ABC, HBO, etc.), intermediatedistribution facilities and/or servers, Internet providers, on-demandmedia servers, and other content providers. NBC is a trademark owned bythe National Broadcasting Company, Inc.; ABC is a trademark owned by theAmerican Broadcasting Company, Inc.; and HBO is a trademark owned by theHome Box Office, Inc. Content source 108 may be the originator ofcontent (e.g., a television broadcaster, a Webcast provider, etc.) ormay not be the originator of content (e.g., an on-demand contentprovider, an Internet provider of content of broadcast programs fordownloading, etc.). Content source 108 may include cable sources,satellite providers, on-demand providers, Internet providers,over-the-top content providers, or other providers of content. Contentsource 108 may also include a remote media server used to storedifferent types of content (e.g., including video content selected by auser) in a location remote from computing device 104. Systems andmethods for remote storage of content and providing remotely storedcontent to user equipment are discussed in greater detail in connectionwith Ellis et al., U.S. Pat. No. 7,761,892, issued Jul. 20, 2010, whichis hereby incorporated by reference herein in its entirety.

Content and/or data delivered to computing device 104 may beover-the-top (OTT) content. OTT content delivery allows Internet-enableduser devices, such as computing device 104, to receive content that istransferred over the Internet, including any content described above, inaddition to content received over cable or satellite connections. OTTcontent is delivered via an Internet connection provided by an Internetservice provider (ISP), but a third party distributes the content. TheISP may not be responsible for the viewing abilities, copyrights, orredistribution of the content, and may transfer only IP packets providedby the OTT content provider. Examples of OTT content providers includeYOUTUBE, NETFLIX, and HULU, which provide audio and video via IPpackets. YouTube is a trademark owned by Google LLC; Netflix is atrademark owned by Netflix, Inc.; and Hulu is a trademark owned by Hulu,LLC. OTT content providers may additionally or alternatively providemedia guidance data described above. In addition to content and/or mediaguidance data, providers of OTT content can distribute applications(e.g., web-based applications or cloud-based applications), or thecontent can be displayed by applications stored on computing device 104.

Having described system 100, reference is now made to FIG. 4, whichdepicts an illustrative flowchart of process 400 for recommendingcontent for a video game that may be implemented by using system 300, inaccordance with some embodiments of the disclosure. In variousembodiments, individual steps of process 400, or any process describedherein, may be implemented by one or more components of system 300.Although the present disclosure may describe certain steps of process400 (and of other processes described herein) as being implemented bycertain components of system 300, this is for purposes of illustrationonly, and it should be understood that other components of system 300may implement those steps instead.

At step 402, control circuitry 302 calculates a pre-game performancemetric based on stored player data and on stored settings for the videogame. The pre-game performance metric measures performance of the teamswith respect to their skills. In one example, the video game is a MOBAvideo game and the stored settings include MOBA video game settings. Inone example the stored player data includes skills of each of theplayers. Such skills in the MOBA video game includes power skills,defense skills, attack skills, damage skills, healing skills, controlskills etc. In one example, stored player data includes ability power(AP) scores such as AP defense (AP Def) score, ability power attack(APA) score, attack damage (AD) score, attack damage defense (AD Def)score, crowd control (CC) scores such as CC Done score, CC defense (CCDef) score, healing score etc. of each of the players of the teams. Atstep 404, the control circuitry 302 determines that the pre-gameperformance metric meets a pre-game threshold. In one embodiment, thepre-game threshold is pre-determined based on pre-game performancemetrics measured for the games that were previously played with the sameteams. In one embodiment, steps 402 and 404 are part of the pre-gameanalysis performed by the control circuitry 302 additional details ofwhich are provided below in connection with FIG. 5.

At step 406, in response to determining that the pre-game performancemetric meets the pre-game threshold, the control circuitry 302calculates an in-game performance metric based on stored metadataassociated with the video game play session content that is indicativeof aspects of game play. The in-game performance metric measuresperformance of the teams with respect to their skills during the actualgame play. At step 408, the control circuitry determines that thein-game performance metric meets an in-game performance threshold. Inone embodiment, the in-game performance threshold is pre-determinedbased on video analysis of the games that were played by the sameplayers with the same teams. In one embodiment, steps 406 and 408 arepart of the in-game analysis performed by the control circuitry 302additional details of which are provided below in connection with FIG.6.

At step 410, the control circuitry 302 calculates a post-game aggregatedstatistics from the video game play session content. In one embodiment,the aggregated statistics are calculated by combining the in-gamemetrics of each players determined during in-game analysis of the videogame play session. At step 412, the control circuitry determines thatthe post-game aggregated statistics meets a post-game threshold. At step414, the control circuitry 302 analyzes the aggregated statisticsrelative to the stored player data and stored settings for the videogame. In one embodiment, the control circuitry 302 analyzes theaggregated statistics of in-game analysis with the aggregated statisticsof the pre-game analysis stored in the player data. At step 416 based onthe analyzing, the control circuitry 302 determines whether the videogame play session content is to be recommended. In one embodiment, thecontrol circuitry 302 determines that the aggregated in-game analysis isapproximately same as the aggregated pre-game analysis. In oneembodiment, steps 410, 412, 414 and 416 are part of the post-gameanalysis performed by the control circuitry 302 additional details ofwhich are provided below in connection with FIG. 7.

FIG. 5 depicts an illustrative flowchart of a process 500 for performingthe pre-game analysis of a video game in accordance with someembodiments of the disclosure. Process 500, in various embodiments, maycorrespond to steps 402 and 404 of FIG. 4. At step 502, the systemcalculates a pre-game performance metric of each of a first team and asecond team of a video game. An example of the pre-game performancemetrics of each team is shown in FIG. 8. FIG. 8 illustrates a table 800,which includes team A 802 and team B 804. The table 800 also includes aplurality of scores 806 such as defense score, crowd control (CC) score,ability power (AP) score, attack damage (AD) score indicative of thepre-game performance metric of each of the team A 802 and team B 804. Asshown, the scores 806 of team A 802 are approximately equivalent to thescores 806 of team B 804. In one embodiment, the pre-game performancemetric is determined based on the stored player data. An example of thestored player data includes aggregated pre-game metrics for each playerin the team A 802 as illustrated in FIG. 9. FIG. 9 depicts a graph 900illustrating pre-game analysis of three players of team A 802. As shown,the x-axis includes the three players, player 1 902, player 2 904 andplayer 3 906 and y-axis illustrates the scores of the pre-game metrics908. As shown the aggregated pre-game metric score includes AP Def score908 a, AD Def score 908 b, attack damage (AD) score 908 c, CC Done score908 d, CC Def score 908 e, APA score 908 f and healing score 908 g ofeach of the player 1, 902, player 2 904 and player 3 906. Although, notshown, a similar graph can be generated illustrating pre-gameperformance of the three players of team B 804.

At step 504, the system computes an average value of aggregated pre-gameperformance metrics of each of the first and the second teams. Forexample, the average value 808 of the each of the team A 802 and team B804 is determined to be 8 i.e. as illustrated in FIG. 8. At step 506,the system compares this average value of each of the teams with apre-game threshold average value. The pre-game threshold average valueis pre-determined based on pre-game performance metrics measured for thegames that were previously played with the same teams. At step 508, itis determined whether the average value is greater than a pre-gamethreshold average value.

In one embodiment, if at step 506, it is determined that the averagevalue is greater than the pre-game threshold average value, then, thepre-game threshold average value is updated with the average value atstep 510. At step 512, the control circuitry selects the stored playerdata of the pre-game performance metrics of the video game for post-gameanalysis. In one example, the stored player data selected is theaggregated pre-game metrics 908 illustrated in FIG. 9.

In one embodiment, if at step 508, it is determined that the averagevalue is not greater than the pre-game threshold average value, then atstep 514, the video game is discarded and another video-game is selectedfor performing a pre-game analysis repeating from step 502. Accordingly,pre-game analysis of the video game is trained over time on differentvideos of the same game and database is updated with the updatedpre-game threshold average values.

FIG. 6 depicts an illustrative flowchart of a process 600 for performingan in-game analysis of a video game in accordance with some embodimentsof the disclosure. Process 600, in various embodiments, may correspondto steps 406 and 408 of FIG. 4. At step 602, the control circuitry 302calculates an in-game performance metric of a game play of each of thefirst team and a second team of a video game. The in-game performancemetric measures game performance of the teams with respect to theirskills during the actual game play session during time intervals. Asdiscussed above, in one example, the in-game performance metric for eachis determined based on game map and parameters stored in the metadata ofthe MOBA video game. Some of the parameters include characterpositioning (CP), route followed (RF), reaction time (RT), objectivecaptured (OC), time elapsed in objective capturing (TOC), targetdestroyed (TD), minion management (MM), team fight (TF), and advancedtechniques (AT). In one example, character positioning is analyzed assoon as the game starts for both the team and a chart prepared tocompare the positioning. In one example, route is followed to determinestrategy of each of the teams. In one example, reaction time includestime taken to reach to objectives and participate in fights. Time forboth the teams may be calculated and compared. A best game may be whereboth the teams have reached the place/fights with comparable time sothat they can fight to claim advantage. In one example, objectivecaptured is number of objectives that were captured. In one example,objective captured is amount of time taken to capture an objective. Inone example, target destroyed includes number of enemy targets destroyedand time taken to destroy the target. In one embodiment, the time takento destroy the targets are calculated for both teams and compared toidentify optimal time. In one example, the minion management includesminion spawn and minion target. In one example, team fight includesability of each team regarding attacks. In one example advancedtechniques include execution of a particular task in the game.

In one embodiment, the control circuitry 302 utilizes the parameters andthe game map to analyze game progression during the MOBA video gamesession. In one example, the game map includes the location of targetsand objectives (that are spawned at a particular time), differentroutes/paths to reach that target/objective. Such targets are towersthat need protection while playing game. If the main tower is destroyed,game is over. Each team needs to protect their tower while attacking theopponent tower. The objectives are special items that are spawned at aparticular time and capturing them gives an advantage to the team thathas captured it. Thus, in route analysis, the video game analysis ismainly concentrated on what route a player has taken while reaching thetarget/objective. Since there are multiple routes to thetarget/objective a best game will be determined in which the playershave taken the best route to the target/objective based on their currentposition. In another example, each MOBA video game includes objectivesthat are special items that are spawned at a particular time andcapturing them gives an advantage to the to the team that has capturedit. During the in-game analysis, the efficiency with which each team hasreached the objective and captured the objective is analyzed. Forexample, an objective is going to spawn soon, and both the team areaware of this. Each of the teams is to reach the place and capture it assoon as possible. Data will be collected based on how individual teamhas played while reaching and capturing that objective. Such dataincludes how the players have tackled the current engagement (fight inprogress), which route is taken to reach the objective, how team hascollaborated (strategy) and capture the target, what formation is usedaround the target and time take to reach and capture the target. Thus,the control circuitry 302 calculates the in-game performance metricbased on this analysis associated with the video game play sessioncontent that is indicative of aspects of game play. Some examples of thein-game performance metrics include in-game damage metrics, in-gameattack metrics, in-game defense metrics, in-game crowd control metrics,in-game global ability metrics etc.

An example of the in-game performance metrics of each team is shown inFIG. 10A. FIG. 10A illustrates a table 1000, which includes the team A802 and the team B 804. The table 1000 also includes time interval(time) 1004 and a plurality of scores 1006 such as CP score, RF score,RT score, OC score, TOC score, TD score, MM score, TF score and AT scoreindicative of the in-game performance metric of each of the team A 802and team B 804 during each time interval (time) 1004. An example of timeinterval 1004 is 10 minutes. An average score 1008 is also calculatedfor each of the team A 802 and team B 804 during each time 1004. Asshown, the scores 1006 of team A 802 are approximately equivalent to thescores 1006 of team B 804 during each of the time intervals. Also, theaverage score 1008 of team A 802 is approximately equivaled to theaverage score 1008 of team B 804 during each of the time intervals.Thus, in-game performance metric measures game performance of the teamswith respect to their skills during the actual game play session duringeach of the time intervals.

In one embodiment, an average of the video game progression in a timeinterval 1004 (e.g. 10 mins) is computed. FIG. 10B illustrates a table1020, which includes the average score 1022 of the team A 802 and theaverage score 1024 of team B 804, the time 1004 and average/median score1026 of the game. The average score 1022 of each of the teams A 802 andB 804 is calculated using the average scores 1008 of the teams A 802 andB 804 respectively from the table 1000 in FIG. 10A. The average/medianscore 1026 is calculated using the average scores 1022 and 1024 of eachof the teams A 802 and B 804, respectively. In one embodiment, anadequate information about duration when the game was most exciting isdetermined by the average/median score 106. Thus, a better prediction ofthe quality of the game is determined, which is utilized to recommendthe video game. FIG. 10C depicts a graph 1030 illustrating in-gameanalysis of the team A 802 and team B 804. As shown, the x-axis includesthe team A 802 and team B 804 and y-axis includes their respectiveaverage scores 1022 and 1024 during each of the times 1004.

At step 604, the control circuitry 302 compares each of the in-gameperformance metrics with its corresponding in-game threshold among theplurality of in-game thresholds. The in-game thresholds arepre-determined based on video analysis of the games that were played bythe same players with the same teams. For example, an in-game thresholdfor the MOBA video game includes route analysis threshold. In anotherexample, an in-game threshold for the MOBA video game includes objectcapture threshold. At step 606, it is determined whether at least one ofthe in-game performance metrics less than its corresponding in-gamethresholds. If it is determined that none of the in-game performancemetrics is less than its corresponding in-game threshold, then playerdata of the in-game performance metrics of the video game is selectedfor post-game analysis at step 608. In one embodiment, if at step 606,it is determined that at least one of the in-game performance metricsless than its corresponding in-game threshold, then the video game isdiscarded and another video game is selected for performing in-gameanalysis repeating from step 602. Accordingly, in-game analysis of thevideo game is trained over time on different video of the same game toselect the video game for the post-game analysis.

FIG. 7 depicts an illustrative flowchart of process 700 for performing apost-game analysis of a video game in accordance with some embodimentsof the disclosure. Process 700 in various embodiments, may correspond tosteps 410, 412, 414 and 416 of FIG. 4.

At step 704, compare player data of the in-game performance metrics withplayer data of the pre-game performance metrics. An example of thepost-game analysis of the three players is shown in graph FIG. 11 A.FIG. 11A shows a graph 1100. The y-axis discloses a percentage of eachof the scores AP Def score 1108 a, AD Def score 1108 b, attack damage(AD) score 1108 c, CC Done score 1108 d, CC Def score 1108 e, APA score1108 f and healing score 1108 g of each of the player 1, 902, player 2904 and player 3 906. The in-game metrics of the players is used in thepost-game analysis. An example of the post-game analysis of each playerin the first team utilizing data from scores in FIG. 11A is shown ingraph 1100 in FIG. 11B. The graph 1100 of the aggregated metrics of eachplayer in the first team is generated by combining the data of themetrics of the graph 1000 in FIG. 11A. The graph 1100 shows theaggregated metrics of three players of team A 802. As shown, the x-axisincludes the three players, player 1 902, player 2 904 and player 3 906and y-axis illustrates the scores of the metrics 1108. As shown theaggregated metrics score includes AP Def score 1108 a, AD Def score 1108b, attack damage (AD) score 1108 c, CC Done score 1108 d, CC Def score1108 e, APA score 1108 f and healing score 1108 g of each of the player1, 902, player 2 904 and player 3 906. Although, not shown, a similargraph can be generated illustrating the post-game analysis of the threeplayers of team B 804.

An example of post-game analysis of each teams is shown in FIG. 11C.FIG. 11C illustrates a table 1130, which includes the team A 802 andteam B 804. The table 1130 also includes a plurality of scores 1136 suchas AP def score, AD Def score, AD score, CC done score, CC Def score andHealing score indicative of the performance metric of each of the team A802 and team B 804. As shown, the average scores 1138 of team A 802 arerelatively equivalent to the scores 1138 of team B 804. In oneembodiment, the video game content of this game would be considered acompelling game play for recommendation.

At step 704, it is determined whether the in-game performance metrics isapproximately equivalent to the pre-game performance metrics. In oneexample, the post-game analysis of each of the teams as shown in FIG.11C is used to determine whether the in-game performance metrics isapproximately equivalent to the pre-game performance metrics. In oneexample, the post-game analysis of each of the players as shown in FIG.11B is used to determine whether the in-game performance metrics isapproximately equivalent to the pre-game performance metrics. If at step706, it is determined that the in-game performance metrics isapproximately equivalent to the pre-game performance metrics, then thevideo game play session content of the video game is recommended at step708. If at step 706 it is determined that the in-game performancemetrics is not approximately equivalent to the pre-game performancemetrics, then at step 710, the video game is discarded and the in-gameanalysis of video game session content of another video game is selectedfor in-game analysis. Accordingly, post-game analysis of the video gameis trained over time on different video of the same game to select thevideo game play session of the video game for recommendation.

FIG. 12 is your flowchart of process 1200 for recommending content for avideo game that may be implemented by using system 300, in accordancewith some embodiments of the disclosure. In various embodiments,individual steps of process 1200, or any process described herein, maybe implemented by one or more components of system 300. Although thepresent disclosure may describe certain steps of process 1200 (and ofother processes described herein) as being implemented by certaincomponents of system 300, this is for purposes of illustration only, andit should be understood that other components of system 300 mayimplement those steps instead.

At step 1202, the control circuitry 304 analyzes stored player data foreach player of each team associated with the video game play session andstored settings for the MOBA video game. In one embodiment, the playerdata includes metrics related to attack metrics, defense metrics, anddamage metrics to determine to evaluate the video game play sessioncontent. As discussed above, the player data includes pre-game metricsof each of the players in each of the teams. In one embodiment, the step1202 of analyzing stored data is performed by the control circuitry 302additional details of which are provided below in connection with FIG.13. At step 1204, the control circuitry 304 analyzes an in-gameperformance of each player of each team in the video game play sessioncontent based on a game map of the video game play session content andon in-game metrics for each player of each team determined from thevideo game play session content. The in-game metrics includes in-gameattack metrics, in-game defense metrics, and in-game damage metrics. Inone embodiment, the in-game performance is the in-game metrics discussedin detail above. In one embodiment, the step 1204 of analyzing in-gameperformance is performed by the control circuitry 302 additional detailsof which are provided below in connection with FIG. 14. At step 1206,the control circuitry 304 analyzes the aggregated performance metricsafter end of the video game play session to determine post-game metricsin terms of player data for evaluation of the video game sessioncontent. In one example, the player data includes attack, defense, anddamage metrics. In one example, the stored player data indicates thatthe player 1 killed 20 dragons in 30 minutes and thus would find thegame interesting. In one example, the in-game performance indicates thatthe player 1 killed 19 dragons in 30 minutes and thus this would be alsoconsidered to be good game and the control circuitry 304 would recommenda user to watch the video of the MOBA video game play. At step 1208, thecontrol circuitry 304 recommends the video game play session contentbased on the in-game performance of each player of each team.

FIG. 13 depicts an illustrative flowchart of a process 1300 foranalyzing stored data in pre-game analysis in accordance with someembodiments of the disclosure. Process 1300, in various embodiments, maycorrespond to step 1202 of FIG. 12

At step 1302, the control circuitry 304 retrieves stored player of eachof the two teams of the video game play session of the MOBA video game.The player data includes pre-game metrics of each player in each of thefirst and the second teams. For example, the player data is the pre-gamemetrics of each of the three players in the first team as illustrated inthe graph in FIG. 9. At step 1304, the control circuitry, compares thepre-game metrics of each of the players in the first team with thepre-game metrics of each player in the second team. At step 1306 it isdetermined whether the pre-game metrics of each of the players in thefirst team is equivalent to the pre-game metrics of each player in thesecond team. If at step 1306, it is determined that the pre-game metricsof each of the players in the first team is equivalent to the pre-gamemetrics of each player in the second team then the process 1300 leads toin-game analysis where the pre-game metrics of each player is comparedto the in-game metrics. If however, at step 1306, it is determined thatthe pre-game metrics of each of the players in the first team is notequivalent to the pre-game metrics of each player in the second team,then at step 1308, the control circuitry selects stored player data ofvideo game session of another MOBA video game and process 1300 isrepeated starting at step 1302. Accordingly, pre-game analysis of thevideo game is trained over time on different video of the same game toselect the stored player data of the video game content for the in-gameanalysis.

FIG. 14 depicts an illustrative flowchart of a process 1400 foranalyzing in-game performance in in-game analysis in accordance withsome embodiments of the disclosure. Process 1400, in variousembodiments, may correspond to step 1204 of FIG. 12

At step 1402, the control circuitry 304 compares the in-game metrics ofeach player in the first team and the second teams with the pre-gamemetrics of each corresponding player in the first and the second teams.In one example, such comparison is executed during post-game analysis asdiscussed above with respect to FIGS. 11A, 11B and 11C. At step 1404, itis determined whether the in-game metrics is approximately equivalent tostored in-game threshold metrics. It at step 1404, it is determined thatthe in-game metrics is approximately equivalent to the stored in-gamethreshold metrics process 1400 leads to further post-game analysis. Ifat step 1404, it is determined that the in-game metrics is notapproximately equivalent to the stored in-game threshold metrics, thenat step 1406, the control circuitry 304selects in-game metrics ofanother MOBA video game and process 1400 is repeated starting at step1402. Accordingly, in-game analysis of the video game is trained overtime on different video of the same game to select in-game metrics ofthe video game play session content to recommend the video game playsession content of the MOBA video game.

FIG. 15 depicts an illustrative flowchart of a process 1500 of post-gameanalysis of analyzing data of the video game content in accordance withsome embodiments of the disclosure.

At step 1502, the control circuitry 304 retrieves real time calculatedaggregated player data of each of two teams upon end of the video gameplay session. At step 1504, it is determined whether the post-gamemetrics is comparable to pre-game metrics and approximately equivalentto stored post-game threshold metrics. If at step 1504, it is determinedthat the post-game metrics is comparable to pre-game metrics andapproximately equivalent to stored post-game threshold metrics, then atstep 1506, the video game play session content of the MOBA video game isrecommended. However, it at step 1504, it is determined that thepost-game metrics is not comparable to pre-game metrics and notapproximately equivalent to the stored post-game threshold metrics, thenat step 1508, a different MOBA video game is selected and process 1500is repeated starting at step 1502.

The systems and processes discussed above are intended to beillustrative and not limiting. One skilled in the art would appreciatethat the actions of the processes discussed herein may be omitted,modified, combined, and/or rearranged, and any additional actions may beperformed without departing from the scope of the invention. Moregenerally, the above disclosure is meant to be exemplary and notlimiting. Only the claims that follow are meant to set bounds as to whatthe present disclosure includes. Furthermore, it should be noted thatthe features and limitations described in any one embodiment may beapplied to any other embodiment herein, and flowcharts or examplesrelating to one embodiment may be combined with any other embodiment ina suitable manner, done in different orders, or done in parallel. Inaddition, the systems and methods described herein may be performed inreal time. It should also be noted that the systems and/or methodsdescribed above may be applied to, or used in accordance with, othersystems and/or methods.

1. A computer-implemented method for evaluating video game play sessioncontent for a multiplayer online battle arena (MOBA) video game, themethod comprising: analyzing stored player data for each player of eachteam associated with the video game play session and stored settings forthe MOBA video game, the player data comprising metrics related toattack metrics, defense metrics, and damage metrics to determine toevaluate the video game play session content; in response to determiningto evaluate the video game play session content, analyzing an in-gameperformance of each player of each team in the video game play sessioncontent based on a game map of the video game play session content andon in-game metrics for each player of each team determined from thevideo game play session content, the in-game metrics comprising in-gameattack metrics, in-game defense metrics, and in-game damage metrics; anddetermining to recommend the video game play session content based onthe in-game performance of each player of each team.
 2. Thecomputer-implemented method of claim 1 wherein the stored player datacomprises pre-game metrics of each player among a plurality of playersin a first team and a second team.
 3. The computer-implemented method ofclaim 2 wherein analyzing the stored player data comprising: comparingthe pre-game metrics of each player in the first team with the pre-gamemetrics of each corresponding player in the second team, wherein thecomparing comprising determining whether the pre-game metrics of eachplayer in the first team is approximately equivalent to the pre-gamemetrics of each corresponding player in the second team.
 4. Thecomputer-implemented method of claim 3 further comprising determining toevaluate the video game play session content based on the comparison. 5.The computer-implemented method of claim 1 wherein analyzing the in-gameperformance further comprising: comparing the in-game metrics of eachplayer among a plurality of players in a first team with the in-gamemetrics of each corresponding player in a second team, wherein thecomparing comprising determining whether the in-game metrics of eachplayer in the first team is approximately equivalent to the in-gamemetrics of each corresponding player in the second team.
 6. Thecomputer-implemented method of claim 5 further comprising determining torecommend the video game play session based on the comparison.
 7. Thecomputer-implemented method of claim 1 further comprising calculatingthe in-game metrics based on metadata associated with the video gameplay session content that is indicative of aspects of game play.
 8. Thecomputer-implemented method of claim 7 wherein the metadata comprisescharacter metadata, items metadata and global abilities metadata of theMOBA video game.
 9. The computer-implemented method of claim 1 whereinthe game map comprises route of the location of targets and objectives,different routes to reach the targets and objectives or combinationsthereof.
 10. The computer-implemented method of claim 1 wherein thesettings for the MOBA video game comprise a battle ground including twoseparate teams, each of the teams including a plurality of players. 11.A system for evaluating video game play session content for amultiplayer online battle arena (MOBA) video game, comprising: a memoryconfigured to: store player data and settings for the MOBA video game;and a control circuitry coupled to the memory and configured to: analyzestored player data for each player of each team associated with thevideo game play session and stored settings for the MOBA video game, theplayer data comprising metrics related to attack metrics, defensemetrics, and damage metrics to determine to evaluate the video game playsession content; in response to determining to evaluate the video gameplay session content, analyze an in-game performance of each player ofeach team in the video game play session content based on a game map ofthe video game play session content and on in-game metrics for eachplayer of each team determined from the video game play session content,the in-game metrics comprising in-game attack metrics, in-game defensemetrics, and in-game damage metrics; and determine to recommend thevideo game play session content based on the in-game performance of eachplayer of each team.
 12. The system of claim 11 wherein the storedplayer data comprises pre-game metrics of each player among a pluralityof players in a first team and a second team.
 13. The system of claim 12wherein to analyze the stored player data, the control circuitry isfurther configured to: compare the pre-game metrics of each player inthe first team with the pre-game metrics of each corresponding player inthe second team, wherein to compare further comprise to determinewhether the pre-game metrics of each player in the first team isapproximately equivalent to the pre-game metrics of each correspondingplayer in the second team.
 14. The system of claim 13 wherein thecontrol circuitry is further configured to: determine to evaluate thevideo game play session content based on the comparison.
 15. The systemof claim 11 wherein to analyze the in-game performance the controlcircuitry is further configured to: compare the in-game metrics of eachplayer among a plurality of players in a first team with the in-gamemetrics of each corresponding player in a second team, wherein tocompare further comprises to determine whether the in-game metrics ofeach player in the first team is approximately equivalent to the in-gamemetrics of each corresponding player in the second team.
 16. The systemof claim 15 wherein the control circuitry is further configured to:determine to recommend the video game play session based on thecomparison.
 17. The system of claim 11 wherein the control circuitry isfurther configured to: calculate the in-game metrics based on metadataassociated with the video game play session content that is indicativeof aspects of game play.
 18. The system of claim 17 wherein the metadatacomprises character metadata, items metadata and global abilitiesmetadata of the MOBA video game.
 19. The system of claim 11 wherein thegame map comprises route of the location of targets and objectives,different routes to reach the targets and objectives or combinationsthereof.
 20. The system of claim 11 wherein the settings for the MOBAvideo game comprise a battle ground including two separate teams, eachof the teams including a plurality of players. 21.-30. (canceled)